Jason Hom

Publication Details

  • Multiparametric MRI and CT Models of Infarct Core and Favorable Penumbral Imaging Patterns in Acute Ischemic Stroke STROKE Kidwell, C. S., Wintermark, M., De Silva, D. A., Schaewe, T. J., Jahan, R., Starkman, S., Jovin, T., Hom, J., Jumaa, M., Schreier, J., Gornbein, J., Liebeskind, D. S., Alger, J. R., Saver, J. L. 2013; 44 (1): 73-79


    Objective imaging methods to identify optimal candidates for late recanalization therapies are needed. The study goals were (1) to develop magnetic resonance imaging (MRI) and computed tomography (CT) multiparametric, voxel-based predictive models of infarct core and penumbra in acute ischemic stroke patients, and (2) to develop patient-level imaging criteria for favorable penumbral pattern based on good clinical outcome in response to successful recanalization.An analysis of imaging and clinical data was performed on 2 cohorts of patients (one screened with CT, the other with MRI) who underwent successful treatment for large vessel, anterior circulation stroke. Subjects were divided 2:1 into derivation and validation cohorts. Pretreatment imaging parameters independently predicting final tissue infarct and final clinical outcome were identified.The MRI and CT models were developed and validated from 34 and 32 patients, using 943 320 and 1 236 917 voxels, respectively. The derivation MRI and 2-branch CT models had an overall accuracy of 74% and 80%, respectively, and were independently validated with an accuracy of 71% and 79%, respectively. The imaging criteria of (1) predicted infarct core ?90 mL and (2) ratio of predicted infarct tissue within the at-risk region ?70% identified patients as having a favorable penumbral pattern with 78% to 100% accuracy.Multiparametric voxel-based MRI and CT models were developed to predict the extent of infarct core and overall penumbral pattern status in patients with acute ischemic stroke who may be candidates for late recanalization therapies. These models provide an alternative approach to mismatch in predicting ultimate tissue fate.

    View details for DOI 10.1161/STROKEAHA.112.670034

    View details for Web of Science ID 000312883800014

    View details for PubMedID 23233383

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